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I designed and taught a week-long, Python-based course introducing stochastic simulation, entropy, phase transitions, and neural networks to 20 high school and undergraduate students from non-physics backgrounds at Tecnológico de Monterrey.

The course was built around hands-on notebook labs that emphasized model-building, data analysis, and physical intuition over heavy formalism.

What we covered

• Random walks and Monte Carlo methods
• Probability, entropy, and the statistical view of disorder
• The Ising model and emergent collective behavior
• A gentle bridge from physical models to neural networks

Materials

The full set of notebooks and slides is on GitHub.

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